Role Description This role owns the platform that keeps Givzey secure, compliant, reliable, and scalable. You'll work across AWS infrastructure, Infrastructure as Code, CI/CD, AI services, observability, and developer tooling to make sure engineers spend their time building product instead of fighting deployments. You'll partner closely with engineering, ML, and product to design the platform that powers everything from customer-facing APIs to LLM workflows running on Amazon Bedrock and SageMaker. This is not a "keep the lights on" devops role. You'll actively shape how we deploy software, provision infrastructure, manage AI workloads, and scale the engineering organization. Who thrives here - You're the engineer who gets excited about replacing a manual deployment with a one-click pipeline. - Automating infrastructure instead of clicking around the AWS console. - Designing systems that make the rest of engineering move faster. - You think in terms of reliability, observability, automation, and repeatability. - You're comfortable wearing multiple hats. What you'll do - Cloud infrastructure - Design, build, and maintain our AWS infrastructure. - Manage networking, IAM, compute, storage, databases, and security across environments. - Build scalable infrastructure capable of supporting rapid product growth. - Improve resiliency, availability, and disaster recovery. - Infrastructure as Code - Own our Infrastructure as Code strategy using Pulumi. - Build reusable infrastructure components and shared modules. - Eliminate manual infrastructure changes wherever possible. - Review and evolve our cloud architecture as the company grows. - CI/CD - Build and maintain deployment pipelines for applications and infrastructure. - Improve release automation and deployment safety. - Reduce friction in local development and engineering workflows. - Help establish engineering best practices around testing and deployment. - AI Platform - Build and maintain the infrastructure powering our AI systems. - Work with services such as Amazon Bedrock, SageMaker, OpenSearch, and supporting AWS services. - Support LLM evaluation pipelines, RAG infrastructure, vector search, and model deployment. - Partner with ML engineers to operationalize new AI capabilities. - Platform Operations - Monitor production systems and improve observability. - Respond to production incidents and drive root-cause analysis. - Improve system reliability through automation rather than manual processes. - Continuously evaluate performance, cost, and scalability. - Engineering - Collaborate closely with product, engineering, ML, and customer success. - Help define technical standards and infrastructure direction. - Participate in architecture discussions across the platform. - Mentor other engineers on cloud infrastructure and operational best practices. Qualifications - 5+ years building and operating production software systems. - Strong experience with AWS in production environments. - Experience designing Infrastructure as Code using Pulumi, Terraform, or CloudFormation. - Experience building CI/CD pipelines using GitHub Actions. - Strong Python experience. - Experience building APIs and backend systems. Requirements - You should be comfortable working with technologies such as: - AWS (multi-account environments using AWS Organizations) - ECS - Docker - IAM - VPC networking - RDS - S3 - Lambda - CloudWatch - SNS/SQS - Event-driven architectures - Experience with some of the following is highly desirable: - Amazon Bedrock - SageMaker - Vector databases - Retrieval-Augmented Generation (RAG) - LLM evaluation pipelines - Model deployment - ML infrastructure - Dagster or similar orchestration platforms Working Style - You automate repetitive work instead of documenting it. - You care about reliability as much as shipping features. - You enjoy improving developer experience. - You think systems should become simpler over time. - You take ownership rather than waiting for someone else to fix infrastructure problems. Mindset - Strong written communication. - Comfortable working in ambiguity. - Curious about modern AI infrastructure and where it's headed. - Interested in building systems that engineers enjoy working in. - Excited by the challenge of building infrastructure from the ground up rather than inheriting a mature platform. Nice to have - Pulumi experience - Dagster experience - Amazon Bedrock - SageMaker - OpenSearch - ECS - PostgreSQL - Redis - New Relic or modern observability platforms - Experience supporting AI or ML products - SOC 2 or security/compliance experience - Startup experience What this isn't - This isn't a traditional DevOps role where tickets get tossed over the wall after development. - This isn't an SRE role focused exclusively on uptime. - This isn't an ML engineering role building models. - You're building the platform that allows all of those disciplines to move faster. - You'll own infrastructure decisions, improve how software gets delivered, and help shape the technical foundation of an AI company that's still early enough for your decisions to matter years from now.

• You will design and build the foundational development & infrastructure powering our agentic security platform — secure multi-tenant systems, distributed services, scalable execution layers, and the internal platform capabilities that allow our AI agents to operate reliably in production environments. • You’ll work closely with founders, product, AI, and security engineering teams to build systems that are secure, observable, resilient, and scalable from day one. • Own end-to-end design and delivery of platform capabilities. • Build and scale distributed backend services powering AI agents and customer workflows. • Improve authentication, authorization, and tenant isolation across the platform. • Design infrastructure and observability systems for reliability at scale. • Deploy and operate production-grade systems with strong ownership beyond launch. • Collaborate cross-functionally with engineering, product, customer-facing teams, and leadership to shape platform architecture and engineering standards. • Build high-scale, multi-tenant backend systems that securely support multiple customer environments. • Design and operate distributed microservices with strong reliability and fault tolerance. • Build asynchronous execution pipelines, event-driven architectures, and workflow orchestration systems. • Improve scalability, throughput, concurrency, and system performance across services. • Design resilient APIs, service communication patterns, and infrastructure abstractions.

• Contribute to building and designing the self-service platform • Architect and build self-service infrastructure frameworks • Codify and abstract AWS footprint into reusable infrastructure-as-code modules • Build and implement evaluation frameworks and observability practices • Evaluate ecosystem needs holistically to spot platform investments • Align architectural deliverables with application workflows • Provide technical escalation availability during agreed windows

Role Description We are looking for AWS Cloud Data Platform Engineers to join a project delivered for our international client operating in a data-driven and cloud-first environment. The client is building modern cloud-native data platforms that enable advanced analytics, business intelligence, and data-driven decision-making across the organization. This role is a great opportunity to work on scalable AWS-based data platforms, contributing to the design, development, and continuous improvement of modern data ecosystems. You will combine cloud platform engineering, data engineering, and software development to deliver reliable, secure, and high-performance data solutions supporting business-critical processes. The project combines cloud technologies, data platform development, ETL/ELT engineering, and platform automation within a collaborative international environment. You will work closely with business stakeholders, architects, data engineers, and software development teams to deliver scalable and maintainable cloud-based data solutions. - Design, develop, and maintain modern data platforms based on AWS cloud services. - Build and optimize data lakes, lakehouses, and data warehouse solutions. - Develop and maintain ETL and ELT pipelines for batch and streaming data processing. - Integrate data from multiple sources, including databases, APIs, and event-driven systems. - Ensure data quality, reliability, observability, and operational stability across data platforms. - Develop data processing solutions using Python, SQL, Spark, and related technologies. - Work with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, and similar cloud-native services. - Implement and maintain workflow orchestration using technologies such as Apache Airflow, AWS Step Functions, or equivalent solutions. - Contribute to CI/CD pipelines and deployment automation for data engineering workloads. - Monitor platform health, troubleshoot data-related incidents, and implement preventive improvements. - Optimize platform performance, scalability, and cloud cost efficiency. - Collaborate closely with business stakeholders, architects, analysts, and engineering teams to deliver high-quality data products. - Contribute to engineering best practices, platform standards, automation, and continuous improvement initiatives. - Depending on seniority, take ownership of technical areas, support architectural decisions, and mentor less experienced team members. Qualifications - Commercial experience in data engineering, cloud engineering, or data platform development. - Strong hands-on experience with AWS cloud services. - Practical experience designing and building cloud-based data platforms. - Experience developing data lakes, lakehouses, or enterprise data warehouse solutions. - Strong experience building ETL and ELT pipelines in production environments. - Practical experience integrating multiple data sources, including relational databases, APIs, and streaming platforms. - Strong programming skills in Python and SQL. - Experience with Apache Spark or similar distributed data processing frameworks. - Practical experience with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, or equivalent technologies. - Experience implementing workflow orchestration using Apache Airflow, AWS Step Functions, or similar tools. - Understanding of CI/CD practices and deployment automation for cloud-based data solutions. - Experience implementing monitoring, observability, and operational support for data platforms. - Strong understanding of cloud performance optimization and cost management. - Strong communication, collaboration, and problem-solving skills. - Experience working within Agile software delivery environments. - Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery. - Work from the European Union region and a work permit are required. Nice to have - Experience working with real-time or event-driven data processing architectures. - Experience with Infrastructure as Code technologies such as Terraform or AWS CloudFormation. - Familiarity with containerization and orchestration technologies such as Docker and Kubernetes. - Experience implementing data governance, security, and compliance practices. - Experience contributing to enterprise-scale cloud data platform architecture. - Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work. - Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. Recruitment Process - CV review - HR call - Interview - Client Interview - Decision